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- W2891363964 abstract "Completing a partially-known matrix, is an important problem in the field of data science and useful for many related applications, e.g., collaborative filtering for recommendation systems, global positioning in large-scale sensor networks. Low-rank and Gaussian models are two popular classes of models used in matrix completion, both of which have proven success. In this paper, we introduce a single model that leverage the features of both low-rank and Gaussian models. We develop a novel method based on Expectation Maximization (EM) that involves spectral regularization (for low-rank part) as well as maximum likelihood maximization (for learning Gaussian parameters). We also test our framework on real-world movie rating data, and provide comparison results with some of the common methods used for matrix completion." @default.
- W2891363964 created "2018-09-27" @default.
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- W2891363964 date "2018-07-01" @default.
- W2891363964 modified "2023-09-27" @default.
- W2891363964 title "Learning a Joint Low-Rank and Gaussian Model in Matrix Completion with Spectral Regularization and Expectation Maximization Algorithm" @default.
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- W2891363964 doi "https://doi.org/10.1109/bigdatacongress.2018.00035" @default.
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